Course: 2019/2020

Mathematics for Data Science

(17752)

While there are many applied mathematics techniques and concepts that are useful (and used) in Data Science, this course focus on the basics of those based on linear algebra and calculus, as they underlie many of the most importants applications and algorithms: Matrix algebra, Matrix decompositions.

Description of contents: programme

1. Linear Systems
2. Vectors
3. Matrices
4. Diagonalization
5. Orthogonality
6. Symmetric Matrices

Learning activities and methodology

Theoretical classes (lectures)
Practical problems that students must solve individually as homework
Tutorials

Assessment System

- % end-of-term-examination 100
- % of continuous assessment (assigments, laboratory, practicals...) 0

Basic Bibliography

- David C. Lay, Steven R. Lay, Judi J. McDonald. Linear Algebra and Its Applications. Pearson; 5 edition. 2016

Additional Bibliography

- Gilbert Strang. LINEAR ALGEBRA and learning from Data. Wellesley Cambridge Press. 2019
- W. Keith Nicholson. Linear Algebra with Applications. McGraw-Hill, 6th edition. 2009

- Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong · Mathematics for Machine Learning : https://mml-book.github.io/

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